[GitHub] [flink-ml] jiangxin369 commented on a diff in pull request #191: [FLINK-30401] Add Estimator and Transformer for MinHashLSH
jiangxin369 commented on code in PR #191: URL: https://github.com/apache/flink-ml/pull/191#discussion_r1060307196 ## flink-ml-python/pyflink/ml/lib/feature/lsh.py: ## @@ -0,0 +1,191 @@ + +# Licensed to the Apache Software Foundation (ASF) under one +# or more contributor license agreements. See the NOTICE file +# distributed with this work for additional information +# regarding copyright ownership. The ASF licenses this file +# to you under the Apache License, Version 2.0 (the +# "License"); you may not use this file except in compliance +# with the License. You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + + +import typing +from abc import ABC +from pyflink.java_gateway import get_gateway +from pyflink.table import Table +from pyflink.util.java_utils import to_jarray + +from pyflink.ml.core.linalg import Vector, DenseVector, SparseVector +from pyflink.ml.core.param import Param, IntParam, ParamValidators +from pyflink.ml.core.wrapper import JavaWithParams +from pyflink.ml.lib.feature.common import JavaFeatureEstimator, JavaFeatureModel +from pyflink.ml.lib.param import HasInputCol, HasOutputCol, HasSeed + + +class _LSHModelParams(JavaWithParams, + HasInputCol, + HasOutputCol): +""" +Params for :class:`LSHModel` +""" + +def __init__(self, java_params): +super(_LSHModelParams, self).__init__(java_params) + + +class _LSHParams(_LSHModelParams): +""" +Params for :class:`LSH` +""" + +NUM_HASH_TABLES: Param[int] = IntParam( +"num_hash_tables", "Number of hash tables.", 1, ParamValidators.gt_eq(1) +) + +NUM_HASH_FUNCTIONS_PER_TABLE: Param[int] = IntParam( +"num_hash_functions_per_table", +"Number of hash functions per table.", +1, +ParamValidators.gt_eq(1.)) + +def __init__(self, java_params): +super(_LSHParams, self).__init__(java_params) + +def set_num_hash_tables(self, value: int): +return typing.cast(_LSHParams, self.set(self.NUM_HASH_TABLES, value)) + +def get_num_hash_tables(self): +return self.get(self.NUM_HASH_TABLES) + +@property +def num_hash_tables(self): +return self.get_num_hash_tables() + +def set_num_hash_functions_per_table(self, value: int): +return typing.cast(_LSHParams, self.set(self.NUM_HASH_FUNCTIONS_PER_TABLE, value)) + +def get_num_hash_functions_per_table(self): +return self.get(self.NUM_HASH_FUNCTIONS_PER_TABLE) + +@property +def num_hash_functions_per_table(self): +return self.get_num_hash_functions_per_table() + + +class _LSH(JavaFeatureEstimator, ABC): +""" +Base class for estimators which implement LSH (Locality-sensitive hashing) algorithms. +""" + +def __init__(self): +super(_LSH, self).__init__() + +@classmethod +def _java_estimator_package_name(cls) -> str: +return "lsh" + + +class _LSHModel(JavaFeatureModel, ABC): +""" +Base class for LSH model. +""" + +def __init__(self, java_model): +super(_LSHModel, self).__init__(java_model) + +@classmethod +def _java_model_package_name(cls) -> str: +return "lsh" + +def approx_nearest_neighbors(self, dataset: Table, key: Vector, k: int, + dist_col: str = 'distCol'): +""" +Given a dataset and an item, approximately find at most k items which have the closest +distance to the item. If the `outputCol` is missing in the given dataset, this method +transforms the dataset with the model at first. + +:param dataset: The dataset in which to to search for nearest neighbors. +:param key: The item to search for. +:param k: The maximum number of nearest neighbors. +:param dist_col: The output column storing the distance between each neighbor and the +key. +:return: A dataset containing at most k items closest to the key with a column named +`distCol` appended. Review Comment: To keep the same coding style with PyFlink and flink-ml-python, it's recommended to align the beginning of each line. E.g., ``` :param dist_col: The output column storing the distance between each neighbor and the key. :return: A dataset containing at most k items closest to the key with a column named `distCol` appended. ``` So as the other com
[GitHub] [flink-ml] jiangxin369 commented on a diff in pull request #191: [FLINK-30401] Add Estimator and Transformer for MinHashLSH
jiangxin369 commented on code in PR #191: URL: https://github.com/apache/flink-ml/pull/191#discussion_r1060307196 ## flink-ml-python/pyflink/ml/lib/feature/lsh.py: ## @@ -0,0 +1,191 @@ + +# Licensed to the Apache Software Foundation (ASF) under one +# or more contributor license agreements. See the NOTICE file +# distributed with this work for additional information +# regarding copyright ownership. The ASF licenses this file +# to you under the Apache License, Version 2.0 (the +# "License"); you may not use this file except in compliance +# with the License. You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + + +import typing +from abc import ABC +from pyflink.java_gateway import get_gateway +from pyflink.table import Table +from pyflink.util.java_utils import to_jarray + +from pyflink.ml.core.linalg import Vector, DenseVector, SparseVector +from pyflink.ml.core.param import Param, IntParam, ParamValidators +from pyflink.ml.core.wrapper import JavaWithParams +from pyflink.ml.lib.feature.common import JavaFeatureEstimator, JavaFeatureModel +from pyflink.ml.lib.param import HasInputCol, HasOutputCol, HasSeed + + +class _LSHModelParams(JavaWithParams, + HasInputCol, + HasOutputCol): +""" +Params for :class:`LSHModel` +""" + +def __init__(self, java_params): +super(_LSHModelParams, self).__init__(java_params) + + +class _LSHParams(_LSHModelParams): +""" +Params for :class:`LSH` +""" + +NUM_HASH_TABLES: Param[int] = IntParam( +"num_hash_tables", "Number of hash tables.", 1, ParamValidators.gt_eq(1) +) + +NUM_HASH_FUNCTIONS_PER_TABLE: Param[int] = IntParam( +"num_hash_functions_per_table", +"Number of hash functions per table.", +1, +ParamValidators.gt_eq(1.)) + +def __init__(self, java_params): +super(_LSHParams, self).__init__(java_params) + +def set_num_hash_tables(self, value: int): +return typing.cast(_LSHParams, self.set(self.NUM_HASH_TABLES, value)) + +def get_num_hash_tables(self): +return self.get(self.NUM_HASH_TABLES) + +@property +def num_hash_tables(self): +return self.get_num_hash_tables() + +def set_num_hash_functions_per_table(self, value: int): +return typing.cast(_LSHParams, self.set(self.NUM_HASH_FUNCTIONS_PER_TABLE, value)) + +def get_num_hash_functions_per_table(self): +return self.get(self.NUM_HASH_FUNCTIONS_PER_TABLE) + +@property +def num_hash_functions_per_table(self): +return self.get_num_hash_functions_per_table() + + +class _LSH(JavaFeatureEstimator, ABC): +""" +Base class for estimators which implement LSH (Locality-sensitive hashing) algorithms. +""" + +def __init__(self): +super(_LSH, self).__init__() + +@classmethod +def _java_estimator_package_name(cls) -> str: +return "lsh" + + +class _LSHModel(JavaFeatureModel, ABC): +""" +Base class for LSH model. +""" + +def __init__(self, java_model): +super(_LSHModel, self).__init__(java_model) + +@classmethod +def _java_model_package_name(cls) -> str: +return "lsh" + +def approx_nearest_neighbors(self, dataset: Table, key: Vector, k: int, + dist_col: str = 'distCol'): +""" +Given a dataset and an item, approximately find at most k items which have the closest +distance to the item. If the `outputCol` is missing in the given dataset, this method +transforms the dataset with the model at first. + +:param dataset: The dataset in which to to search for nearest neighbors. +:param key: The item to search for. +:param k: The maximum number of nearest neighbors. +:param dist_col: The output column storing the distance between each neighbor and the +key. +:return: A dataset containing at most k items closest to the key with a column named +`distCol` appended. Review Comment: To keep the same coding style with PyFlink and flink-ml-python, it's recommended to align the beginning of each line. E.g., ``` :param dist_col: The output column storing the distance between each neighbor and the key. :return: A dataset containing at most k items closest to the key with a column named `distCol` appended. ``` So as the other com
[GitHub] [flink-ml] jiangxin369 commented on a diff in pull request #191: [FLINK-30401] Add Estimator and Transformer for MinHashLSH
jiangxin369 commented on code in PR #191:
URL: https://github.com/apache/flink-ml/pull/191#discussion_r1060261023
##
flink-ml-examples/src/main/java/org/apache/flink/ml/examples/feature/MinHashLSHExample.java:
##
@@ -0,0 +1,155 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements. See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership. The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License. You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.flink.ml.examples.feature;
+
+import org.apache.flink.api.common.typeinfo.TypeInformation;
+import org.apache.flink.api.common.typeinfo.Types;
+import org.apache.flink.ml.feature.lsh.MinHashLSH;
+import org.apache.flink.ml.feature.lsh.MinHashLSHModel;
+import org.apache.flink.ml.linalg.DenseVector;
+import org.apache.flink.ml.linalg.SparseVector;
+import org.apache.flink.ml.linalg.Vector;
+import org.apache.flink.ml.linalg.Vectors;
+import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
+import org.apache.flink.table.api.Table;
+import org.apache.flink.table.api.bridge.java.StreamTableEnvironment;
+import org.apache.flink.types.Row;
+
+import org.apache.commons.collections.IteratorUtils;
+
+import java.util.Arrays;
+import java.util.List;
+
+import static org.apache.flink.table.api.Expressions.$;
+
+/**
+ * Simple program that trains a MinHashLSH model and uses it for approximate
nearest neighbors and
+ * similarity join.
+ */
+public class MinHashLSHExample {
+public static void main(String[] args) throws Exception {
+
+// Creates a new StreamExecutionEnvironment
+StreamExecutionEnvironment env =
StreamExecutionEnvironment.getExecutionEnvironment();
+
+// Creates a StreamTableEnvironment
+StreamTableEnvironment tEnv = StreamTableEnvironment.create(env);
+
+// Generates two datasets
+Table data =
+tEnv.fromDataStream(
+env.fromCollection(
+Arrays.asList(
+Row.of(
+0,
+Vectors.sparse(
+6,
+new int[] {0, 1, 2},
+new double[] {1., 1.,
1.})),
+Row.of(
+1,
+Vectors.sparse(
+6,
+new int[] {2, 3, 4},
+new double[] {1., 1.,
1.})),
+Row.of(
+2,
+Vectors.sparse(
+6,
+new int[] {0, 2, 4},
+new double[] {1., 1.,
1.}))),
+Types.ROW_NAMED(
+new String[] {"id", "vec"},
+Types.INT,
+
TypeInformation.of(SparseVector.class;
+
+Table dataB =
Review Comment:
As the examples are expected to be the best practice for users, the variable
naming should be more strict. Would it be better if rename the `data` to
`inputTable`, `dataB` to `similarityJoinTable`? `dataA` and `dataB` are also
acceptable. So as in the python example.
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[GitHub] [flink-ml] jiangxin369 commented on a diff in pull request #191: [FLINK-30401] Add Estimator and Transformer for MinHashLSH
jiangxin369 commented on code in PR #191: URL: https://github.com/apache/flink-ml/pull/191#discussion_r1060256575 ## docs/content/docs/operators/feature/minhashlsh.md: ## @@ -0,0 +1,280 @@ +--- +title: "MinHash LSH" +weight: 1 +type: docs +aliases: +- /operators/feature/minhashlsh.html +--- + + + +## MinHash LSH + +MinHash LSH is a Locality Sensitive Hashing (LSH) scheme for Jaccard distance metric. +The input features are sets of natural numbers represented as non-zero indices of vectors, +either dense vectors or sparse vectors. Typically, sparse vectors are more efficient. + +In addition to transforming input feature vectors to multiple hash values, the MinHash LSH +model also supports approximate nearest neighbors search within a dataset regarding a key +vector and approximate similarity join between two datasets. + +### Input Columns + +| Param name | Type | Default | Description| +|:---|:---|:--|:---| +| inputCol | Vector | `"input"` | Features to be mapped. | + +### Output Columns + +| Param name | Type | Default| Description | +|:---|:--|:---|:-| +| outputCol | DenseVector[] | `"output"` | Hash values. | + +### Parameters Review Comment: Since `MinHashLSH` has some parameters that cannot be set to `MinHashLSHModel`, let's separate the parameters into two tables just like in `vectorindexer.md`. ## flink-ml-python/pyflink/ml/lib/feature/lsh.py: ## @@ -0,0 +1,191 @@ + +# Licensed to the Apache Software Foundation (ASF) under one +# or more contributor license agreements. See the NOTICE file +# distributed with this work for additional information +# regarding copyright ownership. The ASF licenses this file +# to you under the Apache License, Version 2.0 (the +# "License"); you may not use this file except in compliance +# with the License. You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + + +import typing +from abc import ABC +from pyflink.java_gateway import get_gateway +from pyflink.table import Table +from pyflink.util.java_utils import to_jarray + +from pyflink.ml.core.linalg import Vector, DenseVector, SparseVector +from pyflink.ml.core.param import Param, IntParam, ParamValidators +from pyflink.ml.core.wrapper import JavaWithParams +from pyflink.ml.lib.feature.common import JavaFeatureEstimator, JavaFeatureModel +from pyflink.ml.lib.param import HasInputCol, HasOutputCol, HasSeed + + +class _LSHModelParams(JavaWithParams, + HasInputCol, + HasOutputCol): +""" +Params for :class:`LSHModel` +""" + +def __init__(self, java_params): +super(_LSHModelParams, self).__init__(java_params) + + +class _LSHParams(_LSHModelParams): +""" +Params for :class:`LSH` +""" + +NUM_HASH_TABLES: Param[int] = IntParam( +"num_hash_tables", "Number of hash tables.", 1, ParamValidators.gt_eq(1) +) + +NUM_HASH_FUNCTIONS_PER_TABLE: Param[int] = IntParam( +"num_hash_functions_per_table", +"Number of hash functions per table.", +1, +ParamValidators.gt_eq(1.)) Review Comment: nit: let's unify the parameter of `gt_eq` in `NUM_HASH_TABLES` and `NUM_HASH_FUNCTIONS_PER_TABLE` to either 1. or 1. ## flink-ml-examples/src/main/java/org/apache/flink/ml/examples/feature/MinHashLSHExample.java: ## @@ -0,0 +1,155 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one + * or more contributor license agreements. See the NOTICE file + * distributed with this work for additional information + * regarding copyright ownership. The ASF licenses this file + * to you under the Apache License, Version 2.0 (the + * "License"); you may not use this file except in compliance + * with the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.flink.ml.examples.feature; + +import org.apache.flink.api.common.typeinfo.TypeInformation; +import org.apache.flink.api.common.typeinfo.Types; +impo
[GitHub] [flink-ml] jiangxin369 commented on a diff in pull request #191: [FLINK-30401] Add Estimator and Transformer for MinHashLSH
jiangxin369 commented on code in PR #191:
URL: https://github.com/apache/flink-ml/pull/191#discussion_r1057228565
##
flink-ml-lib/src/main/java/org/apache/flink/ml/feature/lsh/MinHashLSH.java:
##
@@ -0,0 +1,57 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements. See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership. The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License. You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.flink.ml.feature.lsh;
+
+import org.apache.flink.ml.util.ReadWriteUtils;
+import org.apache.flink.streaming.api.datastream.DataStream;
+import org.apache.flink.table.api.bridge.java.StreamTableEnvironment;
+
+import java.io.IOException;
+
+/**
+ * An Estimator that implements the MinHash LSH algorithm, which supports LSH
for Jaccard distance.
+ *
+ * The input could be dense or sparse vectors. Each input vector must hava
at least one non-zero
Review Comment:
```suggestion
* The input could be dense or sparse vectors. Each input vector must
have at least one non-zero
```
##
flink-ml-lib/src/main/java/org/apache/flink/ml/feature/lsh/LSHModel.java:
##
@@ -0,0 +1,457 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements. See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership. The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License. You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.flink.ml.feature.lsh;
+
+import org.apache.flink.api.common.functions.AggregateFunction;
+import org.apache.flink.api.common.functions.FlatMapFunction;
+import org.apache.flink.api.common.functions.RichFlatMapFunction;
+import org.apache.flink.api.common.functions.RichMapFunction;
+import org.apache.flink.api.common.typeinfo.TypeInformation;
+import org.apache.flink.api.common.typeinfo.Types;
+import org.apache.flink.api.java.functions.KeySelector;
+import org.apache.flink.api.java.tuple.Tuple2;
+import org.apache.flink.api.java.typeutils.RowTypeInfo;
+import org.apache.flink.ml.api.Model;
+import org.apache.flink.ml.common.broadcast.BroadcastUtils;
+import org.apache.flink.ml.common.datastream.DataStreamUtils;
+import org.apache.flink.ml.common.datastream.EndOfStreamWindows;
+import org.apache.flink.ml.common.datastream.TableUtils;
+import org.apache.flink.ml.linalg.DenseVector;
+import org.apache.flink.ml.linalg.Vector;
+import org.apache.flink.ml.param.Param;
+import org.apache.flink.ml.util.ParamUtils;
+import org.apache.flink.streaming.api.datastream.DataStream;
+import org.apache.flink.table.api.Table;
+import org.apache.flink.table.api.bridge.java.StreamTableEnvironment;
+import org.apache.flink.table.api.internal.TableImpl;
+import org.apache.flink.types.Row;
+import org.apache.flink.util.Collector;
+import org.apache.flink.util.Preconditions;
+
+import org.apache.commons.lang3.ArrayUtils;
+
+import java.util.ArrayList;
+import java.util.Collections;
+import java.util.Comparator;
+import java.util.HashMap;
+import java.util.List;
+import java.util.Map;
+import java.util.PriorityQueue;
+
+/**
+ * Base class for LSH model.
+ *
+ * In addition to transforming input feature vectors to multiple hash
values, it also supports
+ * approximate nearest neighbors search within a dataset regarding a key
vector and approximate
+ * similarity join between two datasets.
+ *
+ * @param class type of the LSHModel implementation itself.
+ */
+abstract class LSHModel> implements Model,
LSHModelParams {
+private static final String MODEL_DATA_BC_KEY = "modelData";
+
+private final Map, Object> paramMap = new HashMap<>();
+
+/** Stores the corresponding model data class of T. */
+private final Class modelDataClass;
+
+protected Table modelDataTable;
+
+public LSHModel(Class modelDataClass) {
+this.modelD
